license_name: bria-2.3
license: other
license_link: https://bria.ai/bria-huggingface-model-license-agreement/
library_name: diffusers
inference: false
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- text-to-image
- legal liability
- commercial use
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BRIA 2.3 ControlNet Inpainting Fast
Trained exclusively on the largest multi-source commercial-grade licensed dataset, BRIA 2.3 inpainting guarantees best quality while safe for commercial use. The model provides full legal liability coverage for copyright and privacy infrigement and harmful content mitigation, as our dataset does not represent copyrighted materials, such as fictional characters, logos or trademarks, public figures, harmful content or privacy infringing content.
BRIA 2.3 is an inpainting model designed to fill masked regions in images based on user-provided textual prompts. The model can be applied in different scenarios, including object removal, replacement, addition, and modification within an image, while also possessing the capability to expand the image.
What's New
BRIA 2.3 ControlNet Inpainting can be applied on top of BRIA 2.3 Text-to-Image and therefore enable to use Fast-LORA. This results in extremely fast inpainting model, requires only 1.6s using A10 GPU.
Model Description
- Developed by: BRIA AI
- Model type: Latent diffusion image-to-image model
- License: bria-2.3 inpainting Licensing terms & conditions.
- Purchase is required to license and access the model.
- Model Description: BRIA 2.3 inpainting was trained exclusively on a professional-grade, licensed dataset. It is designed for commercial use and includes full legal liability coverage.
- Resources for more information: BRIA AI
Get Access to the source code and pre-trained model
Interested in BRIA 2.3 inpainting? Our Model is available for purchase.
Purchasing access to BRIA 2.3 inpainting ensures royalty management and full liability for commercial use.
Are you a startup or a student? We encourage you to apply for our specialized Academia and Startup Programs to gain access. These programs are designed to support emerging businesses and academic pursuits with our cutting-edge technology.
Contact us today to unlock the potential of BRIA 2.3 inpainting!
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How To Use
from diffusers import (
AutoencoderKL,
StableDiffusionXLControlNetInpaintPipeline,
LCMScheduler,
)
from pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline
from controlnet import ControlNetModel, ControlNetConditioningEmbedding
import os
from torchvision import transforms
import torch
from tqdm import tqdm
import numpy as np
import pandas as pd
from PIL import Image
def download_image(url):
response = requests.get(url)
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
def get_masked_image(path_to_images_dir, image_name, image, image_mask, width, height):
image_mask = image_mask # inpaint area is white
image_mask = add_margins_to_ratio(image_mask, 1.5)
image_mask = image_mask.resize((width, height)) # object to remove is white (1)
image_mask_pil = image_mask
orig_image = np.array(image.convert("RGB")).astype(np.uint8)
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask_pil.convert("L")).astype(np.float32) / 255.0
assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
masked_image_to_present = image.copy()
masked_image_to_present[image_mask > 0.5] = (0.5,0.5,0.5) # set as masked pixel
image[image_mask > 0.5] = 0.5 # set as masked pixel - s.t. will be grey
image = Image.fromarray((image * 255.0).astype(np.uint8))
masked_image_to_present = Image.fromarray((masked_image_to_present * 255.0).astype(np.uint8))
return image, image_mask_pil, masked_image_to_present
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = download_image(img_url).resize((1024, 1024))
mask_image = download_image(mask_url).resize((1024, 1024))
# Load, init model
controlnet = ControlNetModel().from_config('briaai/DEV-ControlNetInpaintingFast', torch_dtype=torch.float16)
controlnet.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=320,
conditioning_channels = 5
)
controlnet.load_state_dict(torch.load(local_ckpt_dir + local_ckpt_dir_suffix))
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet.to(dtype=torch.float16), torch_dtype=torch.float16, vae=vae) #force_zeros_for_empty_prompt=False, # vae=vae)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA")
pipe.fuse_lora()
pipe = pipe.to('cuda:0')
pipe.enable_xformers_memory_efficient_attention()
generator = torch.Generator(device='cuda:0').manual_seed(123456)
vae = pipe.vae
masked_image, image_mask, masked_image_to_present = get_masked_image(path_to_images_dir, image_name, image_mask, img, width, height)
masked_image_tensor = image_transforms(masked_image)
masked_image_tensor = (masked_image_tensor - 0.5) / 0.5
masked_image_tensor = masked_image_tensor.unsqueeze(0).to(device="cuda")
# masked_image_tensor = masked_image_tensor.permute((0,3,1,2))
control_latents = vae.encode(
masked_image_tensor[:, :3, :, :].to(vae.dtype)
).latent_dist.sample()
control_latents = control_latents * vae.config.scaling_factor
image_mask = np.array(image_mask)[:,:]
mask_tensor = torch.tensor(image_mask, dtype=torch.float32)[None, ...]
# binarize the mask
mask_tensor = torch.where(mask_tensor > 128.0, 255.0, 0)
if normalize_mask_to_0_1:
mask_tensor = mask_tensor / 255.0
mask_tensor = mask_tensor.to(device="cuda")
mask_resized = torch.nn.functional.interpolate(mask_tensor[None, ...], size=(control_latents.shape[2], control_latents.shape[3]), mode='nearest')
# mask_resized = mask_resized.to(torch.float16)
masked_image = torch.cat([control_latents, mask_resized], dim=1)
gen_img = pipe(negative_prompt=default_negative_prompt, prompt=caption,
controlnet_conditioning_sale=1.0,
num_inference_steps=12,
height=height, width=width,
image = masked_image, # control image
init_image = img,
mask_image = mask_tensor,
guidance_scale = 1.2,
generator=generator).images[0]
prompt = "A park bench"
generator = torch.Generator(device='cuda:0').manual_seed(123456)
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image,generator=generator,guidance_scale=5,strength=1).images[0]
image.save("./a_park_bench.png")